Systems | Development | Analytics | API | Testing

Debugging has always been part of the craft. But in today’s systems — distributed, asynchronous, and increasingly opaque — debugging is no longer just difficult. It’s fragmented. Despite better tooling, more telemetry, and the rise of AI-assisted workflows, many developers still experience the same core frustrations when trying to understand what’s actually happening in production.

The recent security breach at Vercel, where a$2 million ransom was demanded after the Context AI OAuth breach, is a wake-up call. Vercel continues to be a pillar of the modern web, serving millions of frontend applications to enterprises around the world. A compromise on such a scale has a ripple effect throughout the enterprise ecosystem.The incident points to a particular weak point: a combination of third-party AI integrations and internal system security.

Quality Assurance used to be the responsibility of a single department. But today, the most effective software teams treat it as a shared responsibility, and the results speak for themselves. There’s a quote from one of Ghost Inspector’s customers that highlights this shift: “The victory for us is how Ghost Inspector has changed the face of QA in our company. We are beginning to grow what I believe is a QA culture.

Breakpoints are one of the most useful tools we can call on when we’re debugging applications. If you’re not familiar, they allow us to pause execution and examine what the program is doing at that moment. And Android Studio offers a whole bunch of add-ons to supplement its core functionality. In this guide, we’ll show you how Android Studio breakpoints work and how you can maximize their potential in your day-to-day work.

Many teams dedicate time and resources to API performance testing, yet still face sluggish releases and delayed deployments. Incidents slip through, and users encounter slow applications. The root cause? Too often, teams treat performance testing as a checkbox, without truly simulating real-world loads or analyzing key performance metrics such as latency, throughput, and error rates. This leads to a false sense of readiness that quickly unravels in production environments.

Most teams deploying AI agents on their data are watching the wrong things. They check whether the query ran and whether the number looks plausible. When both checks pass, the agent gets credit for a correct answer, and the output flows into dashboards, decisions, and the next agent in the chain. There's a gap between those two checks and actual correctness, and it's where the expensive mistakes live. Getting to a correct answer requires more than a formally valid calculation.

Unaccounted data copies within non-production environments can make enterprises vulnerable to cyber theft. Non-production environments — which are often less secure than production environments — are treasure troves for hackers seeking to steal customer data. How many copies of test data are currently floating around your organization’s non-production environments?

BigQuery unveils new capabilities for lakehouse, knowledge graph, built-in AI, and agentic-first experiences.

Leadership keeps asking for more dashboards, faster answers, and tighter compliance. The data team hears a different message: do more with the same staff (or, fewer). That is where the difficulty evaluating on-prem and private cloud deployment models for corporate data analytics and visualization solutions starts to bite.